Users are the true end points of various network applications (e.g., Internet video, web browsing). They sustain the advertisement-based and subscription-based revenue models that enable the growth of these applications. However, the design and evaluation of network applications are traditionally based on network-centric metrics (e.g., throughput, latency).Typically, the impact of these metrics on user behavior and quality of experience (QoE) are studied separately using controlled user studies involving a few tens of users. But, with the recent advancements in big data technologies, we now have the ability to collect large-scale measurements of network centric metrics and user access patterns in the wild. Leveraging on these measurements, this thesis explores the use of big data techniques including machine learning approaches to characterize and capture various user access patterns, and develop user-centric models of quality of experience and user behavior in the wild. Different players such as content providers, ISPs and CDNs can improve content delivery by using these models.
Srinivasan Seshan (Chair)
Aditya Akella (University of Wisconsin-Madison)